Rename DbModels Embeddings, EmbeddingsAdapter to Entry, EntryAdapter

Improves readability as name has closer match to underlying
constructs

- Entry is any atomic item indexed by Khoj. This can be an org-mode
  entry, a markdown section, a PDF or Notion page etc.

- Embeddings are semantic vectors generated by the search ML model
  that encodes for meaning contained in an entries text.

- An "Entry" contains "Embeddings" vectors but also other metadata
  about the entry like filename etc.

src/database/adapters/__init__.py

@@ -27,7 +27,7 @@ from database.models import (
     KhojApiUser,
     NotionConfig,
     GithubConfig,
-    Embeddings,
+    Entry,
     GithubRepoConfig,
     Conversation,
     ConversationProcessorConfig,
@@ -286,54 +286,54 @@ class ConversationAdapters:
         return await OpenAIProcessorConversationConfig.objects.filter(user=user).afirst()
 
 
-class EmbeddingsAdapters:
+class EntryAdapters:
     word_filer = WordFilter()
     file_filter = FileFilter()
     date_filter = DateFilter()
 
     @staticmethod
-    def does_embedding_exist(user: KhojUser, hashed_value: str) -> bool:
-        return Embeddings.objects.filter(user=user, hashed_value=hashed_value).exists()
+    def does_entry_exist(user: KhojUser, hashed_value: str) -> bool:
+        return Entry.objects.filter(user=user, hashed_value=hashed_value).exists()
 
     @staticmethod
-    def delete_embedding_by_file(user: KhojUser, file_path: str):
-        deleted_count, _ = Embeddings.objects.filter(user=user, file_path=file_path).delete()
+    def delete_entry_by_file(user: KhojUser, file_path: str):
+        deleted_count, _ = Entry.objects.filter(user=user, file_path=file_path).delete()
         return deleted_count
 
     @staticmethod
-    def delete_all_embeddings(user: KhojUser, file_type: str):
-        deleted_count, _ = Embeddings.objects.filter(user=user, file_type=file_type).delete()
+    def delete_all_entries(user: KhojUser, file_type: str):
+        deleted_count, _ = Entry.objects.filter(user=user, file_type=file_type).delete()
         return deleted_count
 
     @staticmethod
     def get_existing_entry_hashes_by_file(user: KhojUser, file_path: str):
-        return Embeddings.objects.filter(user=user, file_path=file_path).values_list("hashed_value", flat=True)
+        return Entry.objects.filter(user=user, file_path=file_path).values_list("hashed_value", flat=True)
 
     @staticmethod
-    def delete_embedding_by_hash(user: KhojUser, hashed_values: List[str]):
-        Embeddings.objects.filter(user=user, hashed_value__in=hashed_values).delete()
+    def delete_entry_by_hash(user: KhojUser, hashed_values: List[str]):
+        Entry.objects.filter(user=user, hashed_value__in=hashed_values).delete()
 
     @staticmethod
-    def get_embeddings_by_date_filter(embeddings: BaseManager[Embeddings], start_date: date, end_date: date):
-        return embeddings.filter(
-            embeddingsdates__date__gte=start_date,
-            embeddingsdates__date__lte=end_date,
+    def get_entries_by_date_filter(entry: BaseManager[Entry], start_date: date, end_date: date):
+        return entry.filter(
+            entrydates__date__gte=start_date,
+            entrydates__date__lte=end_date,
         )
 
     @staticmethod
-    async def user_has_embeddings(user: KhojUser):
-        return await Embeddings.objects.filter(user=user).aexists()
+    async def user_has_entries(user: KhojUser):
+        return await Entry.objects.filter(user=user).aexists()
 
     @staticmethod
     def apply_filters(user: KhojUser, query: str, file_type_filter: str = None):
         q_filter_terms = Q()
 
-        explicit_word_terms = EmbeddingsAdapters.word_filer.get_filter_terms(query)
-        file_filters = EmbeddingsAdapters.file_filter.get_filter_terms(query)
-        date_filters = EmbeddingsAdapters.date_filter.get_query_date_range(query)
+        explicit_word_terms = EntryAdapters.word_filer.get_filter_terms(query)
+        file_filters = EntryAdapters.file_filter.get_filter_terms(query)
+        date_filters = EntryAdapters.date_filter.get_query_date_range(query)
 
         if len(explicit_word_terms) == 0 and len(file_filters) == 0 and len(date_filters) == 0:
-            return Embeddings.objects.filter(user=user)
+            return Entry.objects.filter(user=user)
 
         for term in explicit_word_terms:
             if term.startswith("+"):
@@ -354,32 +354,32 @@ class EmbeddingsAdapters:
             if min_date is not None:
                 # Convert the min_date timestamp to yyyy-mm-dd format
                 formatted_min_date = date.fromtimestamp(min_date).strftime("%Y-%m-%d")
-                q_filter_terms &= Q(embeddings_dates__date__gte=formatted_min_date)
+                q_filter_terms &= Q(entry_dates__date__gte=formatted_min_date)
             if max_date is not None:
                 # Convert the max_date timestamp to yyyy-mm-dd format
                 formatted_max_date = date.fromtimestamp(max_date).strftime("%Y-%m-%d")
-                q_filter_terms &= Q(embeddings_dates__date__lte=formatted_max_date)
+                q_filter_terms &= Q(entry_dates__date__lte=formatted_max_date)
 
-        relevant_embeddings = Embeddings.objects.filter(user=user).filter(
+        relevant_entries = Entry.objects.filter(user=user).filter(
             q_filter_terms,
         )
         if file_type_filter:
-            relevant_embeddings = relevant_embeddings.filter(file_type=file_type_filter)
-        return relevant_embeddings
+            relevant_entries = relevant_entries.filter(file_type=file_type_filter)
+        return relevant_entries
 
     @staticmethod
     def search_with_embeddings(
         user: KhojUser, embeddings: Tensor, max_results: int = 10, file_type_filter: str = None, raw_query: str = None
     ):
-        relevant_embeddings = EmbeddingsAdapters.apply_filters(user, raw_query, file_type_filter)
-        relevant_embeddings = relevant_embeddings.filter(user=user).annotate(
+        relevant_entries = EntryAdapters.apply_filters(user, raw_query, file_type_filter)
+        relevant_entries = relevant_entries.filter(user=user).annotate(
             distance=CosineDistance("embeddings", embeddings)
         )
         if file_type_filter:
-            relevant_embeddings = relevant_embeddings.filter(file_type=file_type_filter)
-        relevant_embeddings = relevant_embeddings.order_by("distance")
-        return relevant_embeddings[:max_results]
+            relevant_entries = relevant_entries.filter(file_type=file_type_filter)
+        relevant_entries = relevant_entries.order_by("distance")
+        return relevant_entries[:max_results]
 
     @staticmethod
     def get_unique_file_types(user: KhojUser):
-        return Embeddings.objects.filter(user=user).values_list("file_type", flat=True).distinct()
+        return Entry.objects.filter(user=user).values_list("file_type", flat=True).distinct()